A Neural Network Model of Multistable Perception.

Abstract

In this paper, the major properties and previous models of multistable perception are briefly reviewed. A neural network model based on Hebbian synaptic modification (the brain-state-in-a-boc model of Anderson and colleagues) is shown to satisfactorily account for a number of these properties. We present evidence demonstrating the importance of both the stimulus and the history (both recent and distant) of the system of disambiguate ambiguous stimuli. In addition, some simple extensions are made to allow the dynamic modification of synaptic connectivities during the course of the stimulus presentation. This enables such properties as the time course of reversals, adaptation, and hysteresis to be simulated.

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Document Details

Document Type
Technical Report
Publication Date
Feb 15, 1984
Accession Number
ADA138081

Entities

People

  • A. H. Kawamoto
  • J. A. Anderson

Organizations

  • Brown University

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Brain
  • Central Nervous System
  • Cognition
  • Cognitive Science
  • Computational Science
  • Difference Equations
  • Differential Equations
  • Eigenvalues
  • Nervous System
  • Neural Networks
  • New York
  • Pattern Recognition
  • Probability
  • Psychology
  • Psychophysiology
  • Simulations
  • Two Dimensional

Readers

  • Electrical Engineering
  • Neural Network Machine Learning.
  • Neuroscience

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks